Genetic Algorithms Component Library
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RiverSoftAVG Genetic Algorithms & Programming Component Library

The Genetic Algorithms & Programming Component Library (GACL) is a powerful genetic algorithms and genetic programming solution for Delphi and Appmethod Win32, Win64, OSX, iOS, Android, and Linux!   Designed for Delphi 2010-Tokyo (Win32/Win64/OSX/iOS/Android/Linux) and Appmethod (Object Pascal), the GACL provides simple yet powerful components for designing, evolving, and using genetic algorithms and genetic programs.  The Genetic Algorithms & Programming Component Library is now in version 6.x.  Version 6.x is a free upgrade for users who bought the GACL after Dec 1st, 2014.  Users who bought the GACL before December 1st, 2014 need to purchase an upgrade from the orders page.

Genetic Algorithms and Genetic Programming help you automatically solve a wide range of problems, from optimization and search problems using genetic algorithms to data fitting, prediction and modelling, or decision strategy and game control using genetic programming.

Key Benefits

bullet 100% Source Code
bullet For Vcl and FMX
bullet For Win32, Win64, OSX, iOS, Android, and Linux 
bullet Integrated IDE Help Insight as well as Help File and Online Documentation
bullet Unlimited Population
bullet Read and write your genetic solutions or progress as XML
bullet 6 selection methods (Roulette, Random, Tournament, Stochastic Tournament, Elitist, or Custom)
bullet 4 Fitness Search Methods (Minimize, Maximize, Weighted Minimize, and Weighted Maximize)
bullet Multi-threaded evolve and fitness evaluation available for XE7+

Genetic Algorithms

bullet Unlimited Chromosome size
bullet Helper “gene” class to read and write arbitrarily-sized integers, enumerations, and floating point numbers into the bits of the chromosome
bullet 3 Genetic Operations (crossover, mutation, and inversion)
bullet 5 Crossover Methods (gene boundary, bit boundary, byte boundary, word boundary, and long word boundary)
bullet Genetic Algorithms XML Schema for saving and loading genetic algorithms problems and solutions

Genetic Programming

bullet Specify your problem more intuitively using functions, constants, and variables
bullet Easily add "personality" to your games and programs by evolving different solutions to the same problem (e.g., a cautious AI, a daring AI, etc)
bullet Completely rebuilt from the ground up Tree-based Genetic Programming with Functions, Constants, and Variables
bullet Generics-based Genetic Programming implementation
bullet 6 Initialization Methods (Full, Grow, Half and Half, Ramped Full, Ramped Grow, and Ramped Half and Half) 
bullet 3 Basic Genetic Operations (crossover, mutation, and inversion)
bullet 6 Different Mutation Methods (Subtree, Replacement, Constant, Shrink, Hoist, and Point)
bullet 17 Different Bloat Control Strategies (Limit Size or Depth, Tarpeian Size or Depth, Unfit Size or Depth, Shrink Size or Depth, Hoist Size or Depth, Size Fair or Depth Fair Crossover, Size or Depth Parsimony Pressure, Covariant Size or Depth Parsimony Pressure, and Lexicographic Parsimony Pressure)
bullet Executor component for executing your winning genetic programs
bullet Genetic Programming XML Schema for saving and loading genetic programming problems and solutions

See the GACL Version History page for full details on what has changed.

For Delphi 2010-Tokyo (Win32/Win64/OSX/iOSX/Android) and Appmethod (Object Pascal) (Earlier versions are available for Delphi 2009 and earlier)

Genetic Algorithms

Genetic algorithms (GA) are computer science techniques that seek to solve optimization or search problems. They are inspired by evolutionary biology and approach the search problem as a task of evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).

Genetic algorithms have been used in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. 

Genetic Programming

Genetic programming (GP) is a computer science method, inspired by evolutionary biology, for automatically solving problems, without having to know or define the form or structure of optimum problem structure beforehand. You define the basic building blocks (functions, constants, and variables) of the problem and then the component does the rest. Genetic programming solves problems by evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination). 

Genetic Programming is a specialization of genetic algorithms where each individual is a computer program.  It has found success as a automatic programming tool, a machine learning tool or an automatic problem-solving engine.  Genetic programming can be used for Curve Fitting, Data Modelling and Symbolic Regression; Decision Strategy, Game Control, and Industrial Process Control; Image and Signal Processing; and Financial Trading, Time Series Prediction and Economic Modelling.

Demos

Check out the evaluation demo on the Downloads page.

The demo has no limitations except:

bullet For Delphi and RAD Studio only. We are sorry but there are no evaluation versions for Appmethod. Appmethod does not include the VCL support routines we need for the evaluation versions.
bullet For Vcl (Win32/64) and FMX (Win 32) Only
bullet When Delphi is not running, applications compiled with the library will display a nag screen

*** The DEMO version is for EVALUATION PURPOSES ONLY ***

Demo applications using the GACL are also available on the Downloads page.

Order

To check our prices or to order, go to the Order page. 

 
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Last modified: April 2, 2017